Abstract
AbstractThere is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.
DOI
10.1038/s41467-022-31609-5
Publication Date
2022-07-01
Publication Title
Nature Communications
Volume
13
Issue
1
ISSN
2041-1723
Embargo Period
2022-08-05
Organisational Unit
School of Biological and Marine Sciences
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Fortino, V., Kinaret, P., Fratello, M., Serra, A., Saarimäki, L., Gallud, A., Gupta, G., Vales, G., Correia, M., Rasool, O., Ytterberg, J., Monopoli, M., Skoog, T., Ritchie, P., Moya, S., Vázquez-Campos, S., Handy, R., Grafström, R., Tran, L., Zubarev, R., Lahesmaa, R., Dawson, K., Loeschner, K., Larsen, E., Krombach, F., Norppa, H., Kere, J., Savolainen, K., Alenius, H., Fadeel, B., & Greco, D. (2022) 'Biomarkers of nanomaterials hazard from multi-layer data', Nature Communications, 13(1). Available at: https://doi.org/10.1038/s41467-022-31609-5
